Network Security Risk Assessment Based on Node Correlation

Similar documents
Chain-linking and seasonal adjustment of the quarterly national accounts

Online Technical Appendix: Estimation Details. Following Netzer, Lattin and Srinivasan (2005), the model parameters to be estimated

The Virtual Machine Resource Allocation based on Service Features in Cloud Computing Environment

Accuracy of the intelligent dynamic models of relational fuzzy cognitive maps

Prediction of Oil Demand Based on Time Series Decomposition Method Nan MA * and Yong LIU

Albania. A: Identification. B: CPI Coverage. Title of the CPI: Consumer Price Index. Organisation responsible: Institute of Statistics

Dynamic Relationship and Volatility Spillover Between the Stock Market and the Foreign Exchange market in Pakistan: Evidence from VAR-EGARCH Modelling

Normal Random Variable and its discriminant functions

The Financial System. Instructor: Prof. Menzie Chinn UW Madison

Baoding, Hebei, China. *Corresponding author

Correlation of default

Differences in the Price-Earning-Return Relationship between Internet and Traditional Firms

The Empirical Research of Price Fluctuation Rules and Influence Factors with Fresh Produce Sequential Auction Limei Cui

Co-Integration Study of Relationship between Foreign Direct Investment and Economic Growth

IFX-Cbonds Russian Corporate Bond Index Methodology

Lab 10 OLS Regressions II

Return Calculation Methodology

Pricing Model of Credit Default Swap Based on Jump-Diffusion Process and Volatility with Markov Regime Shift

Some Insights of Value-Added Tax Gap

Economics of taxation

Fugit (options) The terminology of fugit refers to the risk neutral expected time to exercise an

Michał Kolupa, Zbigniew Śleszyński SOME REMARKS ON COINCIDENCE OF AN ECONOMETRIC MODEL

Tax Dispute Resolution and Taxpayer Screening

Mind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation. Hongliang Yan 2017/06/21

FITTING EXPONENTIAL MODELS TO DATA Supplement to Unit 9C MATH Q(t) = Q 0 (1 + r) t. Q(t) = Q 0 a t,

Empirical Study on the Relationship between ICT Application and China Agriculture Economic Growth

Deriving Reservoir Operating Rules via Fuzzy Regression and ANFIS

Commodity Future Money Flows Trading Strategies Based on HMM

Management of financial and consumer satisfaction risks in supply chain design

SkyCube Computation over Wireless Sensor Networks Based on Extended Skylines

Numerical Evaluation of European Option on a Non Dividend Paying Stock

Terms and conditions for the MXN Peso / US Dollar Futures Contract (Physically Delivered)

Estimation of Optimal Tax Level on Pesticides Use and its

Improving Forecasting Accuracy in the Case of Intermittent Demand Forecasting

Online appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory

An Inclusion-Exclusion Algorithm for Network Reliability with Minimal Cutsets

Effective Feedback Of Whole-Life Data to The Design Process

AN EMPIRICAL STUDY ON RELATIONSHIP BETWEEN POPULATION IMMIGRATION AND URBAN HOUSING MARKET*

A Novel Particle Swarm Optimization Approach for Grid Job Scheduling

American basket and spread options. with a simple binomial tree

STOCK PRICES TEHNICAL ANALYSIS

Estimating intrinsic currency values

MORNING SESSION. Date: Wednesday, May 4, 2016 Time: 8:30 a.m. 11:45 a.m. INSTRUCTIONS TO CANDIDATES

ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm

Permanent Income and Consumption

HFR Risk Parity Indices

Section 6 Short Sales, Yield Curves, Duration, Immunization, Etc.

Floating rate securities

Optimal Combination of Trading Rules Using Neural Networks

SOCIETY OF ACTUARIES FINANCIAL MATHEMATICS. EXAM FM SAMPLE SOLUTIONS Interest Theory

AN APPLICATION OF SPATIAL - PANEL ANALYSIS - PROVINCIAL ECONOMIC GROWTH AND LOGISTICS IN CHINA

Fairing of Polygon Meshes Via Bayesian Discriminant Analysis

VI. Clickstream Big Data and Delivery before Order Making Mode for Online Retailers

MULTI-SPECTRAL IMAGE ANALYSIS BASED ON DYNAMICAL EVOLUTIONARY PROJECTION PURSUIT

Keywords: School bus problem, heuristic, harmony search

Methodology of the CBOE S&P 500 PutWrite Index (PUT SM ) (with supplemental information regarding the CBOE S&P 500 PutWrite T-W Index (PWT SM ))

Empirical analysis on China money multiplier

A Hybrid Method for Forecasting with an Introduction of a Day of the Week Index to the Daily Shipping Data of Sanitary Materials

Using Fuzzy-Delphi Technique to Determine the Concession Period in BOT Projects

Time-domain Analysis of Linear and Nonlinear Circuits

Bank of Japan. Research and Statistics Department. March, Outline of the Corporate Goods Price Index (CGPI, 2010 base)

The impact of intellectual capital on returns and stock prices of listed companies in Tehran Stock Exchange

A valuation model of credit-rating linked coupon bond based on a structural model

Recen Emprcal Leraure Sur vey Over he pas few decades, a large amoun of research has been devoed n sudyng he aggregae demand for mpors n developed, de

An improved segmentation-based HMM learning method for Condition-based Maintenance

Real-Time Traffic over the IEEE Medium Access Control Layer

A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm An Application to the Data of Operating equipment and supplies

A Novel Application of the Copula Function to Correlation Analysis of Hushen300 Stock Index Futures and HS300 Stock Index

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

Noise and Expected Return in Chinese A-share Stock Market. By Chong QIAN Chien-Ting LIN

Associating Absent Frequent Itemsets with Infrequent Items to Identify Abnormal Transactions

Quarterly Accounting Earnings Forecasting: A Grey Group Model Approach

The Leslie model and population stability: an application

UNN: A Neural Network for uncertain data classification

Time-Varying Correlations Between Credit Risks and Determinant Factors

DYNAMIC ECONOMETRIC MODELS Vol. 8 Nicolaus Copernicus University Toruń 2008

Assessment of The relation between systematic risk and debt to cash flow ratio

Cointegration between Fama-French Factors

Cryptographic techniques used to provide integrity of digital content in long-term storage

Interest Rate Derivatives: More Advanced Models. Chapter 24. The Two-Factor Hull-White Model (Equation 24.1, page 571) Analytic Results

Unified Unit Commitment Formulation and Fast Multi-Service LP Model for Flexibility Evaluation in Sustainable Power Systems

The UAE UNiversity, The American University of Kurdistan

Impact of Stock Markets on Economic Growth: A Cross Country Analysis

PFAS: A Resource-Performance-Fluctuation-Aware Workflow Scheduling Algorithm for Grid Computing

ESSAYS ON MONETARY POLICY AND INTERNATIONAL TRADE. A Dissertation HUI-CHU CHIANG

The Net Benefit to Government of Higher Education: A Balance Sheet Approach

Explaining Product Release Planning Results Using Concept Analysis

Property of stocks and wealth effects on consumption

Improving Earnings per Share: An Illusory Motive in Stock Repurchases

THE HAWKES PROCESS AND TIME-VARYING JUMP INTENSITY IN FINANCIAL TIME SERIES. Maciej Kostrzewski

EXPLOITING GEOMETRICAL NODE LOCATION FOR IMPROVING SPATIAL REUSE IN SINR-BASED STDMA MULTI-HOP LINK SCHEDULING ALGORITHM

Gaining From Your Own Default

Pricing and Valuation of Forward and Futures

Market and Information Economics

SETTING CUT OFF SCORES FOR SELECTIVE EDITING IN STRUCTURAL BUSINESS STATISTICS: AN AUTOMATIC PROCEDURE USING SIMULATION STUDY.

Short-Term Load Forecasting using PSO Based Local Linear Wavelet Neural Network

Optimum Reserve Capacity Assessment and Energy and Spinning Reserve Allocation Based on Deterministic and Stochastic Security Approach

Complete fuzzy scheduling and fuzzy earned value management in construction projects *

SEI Trademarks and Service Marks. Get ready for interesting English. Improved cycle time. Increased productivity and quality. Software Engineering

Comparing Sharpe and Tint Surplus Optimization to the Capital Budgeting Approach with Multiple Investments in the Froot and Stein Framework.

Transcription:

Journal of Physcs: Conference Seres PAPER OPE ACCESS ewor Secury Rs Assessmen Based on ode Correlaon To ce hs arcle: Zengguang Wang e al 2018 J. Phys.: Conf. Ser. 1069 012073 Vew he arcle onlne for updaes and enhancemens. Ths conen was downloaded from IP address 148.251.232.83 on 18/10/2018 a 00:28

IOP Publshng IOP Conf. Seres: Journal of Physcs: Conf. Seres 1234567890 1069 (2018) 012073 do :10.1088/1742-6596/1069/1/012073 ewor Secury Rs Assessmen Based on ode Correlaon Zengguang Wang 1, Yu Lu 1 and Jndong L 2 1 Shazhuang Campus of Army Engneerng Unversy, Shazhuang 050003, Chna 2 The 69225 Army, Heng 841300, Chna w1223797579@163.com Absrac. A newor secury rs assessmen mehod based on node correlaon s proposed n order o solve he problem ha lle consderaon s gven o node correlaon and dversy n radon newor secury rs assessmens. Ths mehod whch s based on Hdden arov model quanfes secury rs of he node hrough ndrec rss caused by he drec rss and correlaons of nodes. Combned he secury rs and he mporance of he node, he overall rs of he arge newor s calculaed. Ths mehod can evaluae newor secury rs more accuraely and provde bass for he formulaon of newor secury polcy. 1. Inroducon Wh he connuous developmen of nformaon echnology, he Inerne has gradually become an ndspensable par of people's lfe and wor. Wh he connuous developmen of nformaon echnology, he Inerne has gradually become an ndspensable par of people's lfe and wor. An endless sream of aacs poses a grea hrea o newor secury. Tradonal newor secury proecon based on deecon can only be passve defense afer aac, and can no solve he newor secury problem from he roo [1]. ewor secury rs assessmen can evaluae he rs sae of he arge newor before he hrea occurs, and provde suppor for mplemenng he newor secury conrol sraegy. As one of he research hospos n he feld of newor secury, more achevemens have been made n he feld of newor secury rs assessmen. The prevous research on he rs of newor secury manly focuses on he sudy of he rs of a sngle node [2]. To a ceran exen, he quanave problem of newor secury rs s solved. However, n he process of rs assessmen, he mporance of he newor node correlaon (newor node correlaon, C) and he mporance of he node o he newor secury s no consdered. The newor secury rs assessmen resul s no accurae. To solve hs problem, a newor secury rs assessmen mehod based on node correlaon s proposed n hs paper. Ths mehod s based on Hdden arov model o evaluae he secury rs of ndependen newor nodes. Then, we calculae he correlaon beween nodes and he mporance of nodes by probably. The overall rs of he arge newor s calculaed hrough he secury rs and he mporance of he node. 2. Relevan Theorecal Knowledge 2.1. Defnon of ode Correlaon Defne 1: pars. The hoss n he arge newor are recorded as nodes n he newor. The servces and applcaons runnng on he hos are recorded as he man body on he nodes. The componen on he node whch s called C A s conss of he number symmery beween he hos A and he man body. Conen from hs wor may be used under he erms of he Creave Commons Arbuon 3.0 lcence. Any furher dsrbuon of hs wor mus manan arbuon o he auhor(s) and he le of he wor, ournal caon and DOI. Publshed under lcence by IOP Publshng Ld 1

IOP Publshng IOP Conf. Seres: Journal of Physcs: Conf. Seres 1234567890 1069 (2018) 012073 do :10.1088/1742-6596/1069/1/012073 Defne 2: C. C s a specal access relaonshp based on physcal connecon. Aacer gans access o node A hrough newor aac, and ges he permsson of componen C B hrough aac of componen C A docng pon B. The access relaonshp ha can be used s called C from componen C A o componen C B. C A:B can be represened by an ordered fve uple <A, B,, and W>. Among hem, A and B represen nodes n he newor, and and represen he man body on he node. W represens he specfc quanzed value of he C relaonshp, whch can deermne he dfferen values accordng o he specfc newor envronmen and praccal experence [3]. Defne 3: he mporance of node. The mporance of nodes depends manly on he ype of servce, sorage, and daa flow. In shor, he more servces provded by newor nodes, he more mporan s. If he number of hoss n he newor s L, he mporance of nodes s quanfed as Ps, and he mporance of node A n he newor s quanfed as PA, and hen he relave mporance wegh value of node A n he newor can be defned as: V A PA L s=1 P s (1) 2.2. ecessy Elaboraon In he process of newor secury rs assessmen, he correlaon beween nodes wll affec he accuracy of he evaluaon process. In he assessmen of newor secury rs, f here s a C relaonshp beween wo nodes, when he conrol pary s aaced and he prosecuon s no aaced, accordng o he radonal rs assessmen mehod, only he rs of he conrol pary s ncreased and he prosecuon s affeced by he prosecuon. Obvously, hs s no n lne wh he acual suaon of newor operaon [4]. Therefore, consderng he correlaon of nodes n he process of newor rs assessmen can mprove he accuracy of evaluaon resuls. The locaon and servce provded by newor nodes lead o dfferen mporance of nodes relave o he newor. The mporance of nodes also affecs he resuls of rs assessmen. The newor nodes n he core poson and he newor nodes a he edge poson are affeced by he aac, and he mpac on he overall newor rs s dfferen. Therefore, n he process of newor secury rs assessmen, we should consder he mporance of nodes and he mpac on newor secury rss. 3. Hdden arov odel Based on ode Correlaon 3.1. Hdden arov odel The secury suaon of he arge newor can be represened by he hdden arov model, and he hdden arov model can be represened by fve uples. Among hem: (1) S s1, s2,, s, whch represens he secury sae of a newor node. Because he newor s a changng complex sysem, he secury sae of nodes wll change wh me. Therefore, a sochasc process X X S s used o represen he secury sae of nodes a me. (2) Y y1, y2,, y, whch represens a collecon of dfferen ypes of aacs ha can be deeced n a newor. Aacs n he newor wll change wh me. Therefore, a random processz Z Y s used o represen he ypes of aacs deeced a me. (3) A a, whch represens he ransfer marx of he node secury sae. In he formula, a P( X s X s ),1,. 1 (4) B b ( ), whch represens he observaon marx, whch ndcaes he probably of y aac when node s s n sae. In he formula, b ( ) P( y s ),1,1. (5), whch represens nal probably dsrbuon of secury sae. In he formula, P( X s ),1, whch ndcaes he probably ha nodes are a a safe sae a me 0. 0 2

IOP Publshng IOP Conf. Seres: Journal of Physcs: Conf. Seres 1234567890 1069 (2018) 012073 do :10.1088/1742-6596/1069/1/012073 3.2. ode Sae Probably Calculaon The probably ha he node K s n he sae S of me s,, (, ),1,1 can be calculaed by he followng formula. In he formula (2), Therefore, P( X s, Z y ) P( Z y X s ) P( X s ), (, ) P( X s Z y ) P( Z y ) P Z y X s P X s 1, whch ( ) ( ) () 0 0 0 1 1 1 (3) P( X s ) P( X s, X s ) P( X s X s ) P( X s ) a (2) In he formula (4), () (, ), () 1 P( Z y X s ) a () P( Z y X s ) a 1 1 a represens he ransfer probably of sep of node K. (4) 4. Rs Assessmen ehod Based on Hdden arov odel The rs value of nodes n he newor can be defned by he specfc envronmen of newor operaon. Vecor Rs ( r1, r2, r ) s used o represen he rs value correspondng o each sae. 4.1. Calculaon of ode Rs Value Because of he specal access relaons among nodes n he newor, he rs of nodes s nfluenced by he rs of he assocaed nodes n addon o her own secury rs. The rss of nodes can be dvded no wo pars: drec rss (, ) he sae of he node self; (, ) value. DR and ndrec rss IR (, ). DR (, ) refers o he rs brough by IR refers o he nfluence of he neghborng nodes on he node rs The compuaon of DR (, ) does no ae no accoun he correlaon of nodes. I can be calculaed by he probably dsrbuon of T nodes and he correspondng rs vecors. The concree formula s as follows: DR (, ) (, ) r (5), 1 The compuaon of IR (, ) needs o consder he specal access relaonshp beween nodes. Therefore, he calculaon of IR (, ). I s necessary o consder he C relaonshp beween node K and adacen nodes. The defnon of node correlaon n reference [5] s defned [5]. There are nodes ha have C relaons wh node K, whch s called 1, 2. Accordng o he specfc newor envronmen, he quanave value of C relaonshp s deermned as W l,.the value of W l, s [0, 1], and he greaer he value s, he greaer he rs of node K wll be affeced by relaed nodes. The rs value of he node wh K node C a me s recorded as r (, ),1 l,1.the mpac of nodes wh C relaonshps on he rs of node K s recorded as r (, ),1 l,1.i can be calculaed hrough he quanzed value of C relaonshp and he rs value of he relaed nodes. The specfc formula s as follows:, l l r (, ) W r (, ) (6) l l l 3

IOP Publshng IOP Conf. Seres: Journal of Physcs: Conf. Seres 1234567890 1069 (2018) 012073 do :10.1088/1742-6596/1069/1/012073 The ndrec rs of node K s caused by he specal access relaonshp beween nodes. Therefore, he value of he ndrec rs should be aen as he maxmum of he rs of he relaed nodes., 1 l 1 l l l l IR (, ) max r (, ) max W r (, ) (7) The rs of he node s deermned by DR (, ) and IR (, ). I can be calculaed by he followng formula: R ( ) 1 f ( x) DR (, ) f ( x) IR (, ) (8) f( x) s he wegh funcon of ndrec rs, represenng he proporon of ndrec rs n node rs, and x corresponds o he quanzed value of C correspondng o IR (, ).The selecon of wegh funcon should be carred ou accordng o he specfc condons of he newor, so as o oban more rs values ha are more n lne wh he acual suaon of he newor. The followng characerscs should be me: (1) relaed o he quanzaon value of C relaon; (2) monoonc ncreasng funcon on [0, 1] nerval [6]. 4.2. Calculaon of ewor Rs Value When here are nodes n he arge newor, he correspondng rs values of nodes are recorded as R (, ) m hrough he calculaon mehod of he node rs n 4.1. In order o be more realsc n he rs value of he newor, he radonal sngle hos rs s no drecly added and hen he newor rs value s calculaed on average. Consderng dfferen nodes' nfluence on newor secury, he mporance parameers of nodes are se. The newor rs value R (, ) s calculaed hrough node mporance and node rs value. The specfc calculaon formula s as follows: R (, ) VR (, ),1 m (9) m1 5. Concluson In hs paper, a node correlaon based newor secury rs assessmen mehod s proposed by nroducng node relevance, whch solves he problem ha he radonal newor secury rs assessmen s gnored by he neglec of node correlaon. A he same me, consderng he relave mporance of nodes n he evaluaon process, s closer o he acual suaon of newor operaon. Ths mehod can evaluae newor secury rs more accuraely and provde suppor for he formulaon of newor secury polcy. 6. References [1] Zhou We, Zhang Hong, L Bohan. ewor rs assessmen mehod based on aac-defense graph model [J]. Journal of Souheas Unversy (aural Scence Edon), 2016, 46 (4): 688-694. [2] L Wemng, Le Je, Dong Jng, e al. An opmzed mehod for real-me newor secury quanfcaon [J]. Chnese Journal of Compuers, 2009, 32 (4):793-804. [3] Poolsappas, Dewr R, Ray I. Dynamc secury rs managemen usng Bayesan aac graphs [J]. IEEE Trans on Dependable and Secure Compung, 2012, 9(1):61-74. [4] Zhang Shuwe, LIU Wen-fen, WAG Jun. ewor Secury Quanfcaon Assessmen Based on he Correlaons of odes [J]. Journal of Informaon Engneerng Unversy, 2015, 16 (2):145-151. [5] Zhang Yongzheng, Xa Jngbo, Fang Zyang, e al. ewor secury assessmen based on node quanfcaon [J]. Chnese Journal of Compuers, 2007, 30 (2): 234-240. [6] X Rongrong, Yun Xaochun, Zhang Yongzheng, Hao Zhyu. An Improved Quanave Evaluaon ehod for ewor Secury [J]. Chnese Journal of Compuers, 38 (4), 2015, 749 758. 4